Spore type-specific gene expression profiles underlying development and leaf infection processes of Colletotrichum graminicola

Spore type-specific gene expression profiles underlying development and leaf infection processes of Colletotrichum graminicola | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Spore type-specific gene expression profiles underlying development and leaf infection processes of Colletotrichum graminicola View ORCID Profile Disha Rathi , View ORCID Profile Karsten Andresen , View ORCID Profile Rolf Daniel , View ORCID Profile Marco Alexandre Guerreiro , View ORCID Profile Matthias Kretschmer , View ORCID Profile James Kronstad , View ORCID Profile Minou Nowrousian , View ORCID Profile Stefanie Pöggeler , View ORCID Profile Anja Poehlein , View ORCID Profile Lars M Voll , Daniela Elisabeth Nordzieke doi: https://doi.org/10.1101/2025.11.19.689217 Disha Rathi 1 Institute of Microbiology and Genetics, Genetics of Eukaryotic Microorganisms, University of Göttingen , GZMB, Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Disha Rathi Karsten Andresen 2 Institute of Molecular Physiology, Johannes Gutenberg University , Mainz, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karsten Andresen Rolf Daniel 3 Genomic and Applied Microbiology & Göttingen Genomics Laboratory, Institute of Microbiology and Genetics, Georg-August University of Göttingen , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rolf Daniel Marco Alexandre Guerreiro 4 Environmental Genomics Group, Botanical Institute, Christian-Albrechts University of Kiel , Kiel, Germany 5 Max Planck Institute for Evolutionary Biology , Plön, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marco Alexandre Guerreiro Matthias Kretschmer 6 The Michael Smith Laboratories, Department of Microbiology and Immunology, University of British Columbia , Vancouver, British Columbia, Canada , V6T 1Z4 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Matthias Kretschmer James Kronstad 6 The Michael Smith Laboratories, Department of Microbiology and Immunology, University of British Columbia , Vancouver, British Columbia, Canada , V6T 1Z4 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James Kronstad Minou Nowrousian 7 Department of Molecular and Cellular Botany, Ruhr-Universität Bochum , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Minou Nowrousian Stefanie Pöggeler 1 Institute of Microbiology and Genetics, Genetics of Eukaryotic Microorganisms, University of Göttingen , GZMB, Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stefanie Pöggeler Anja Poehlein 3 Genomic and Applied Microbiology & Göttingen Genomics Laboratory, Institute of Microbiology and Genetics, Georg-August University of Göttingen , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anja Poehlein Lars M Voll 8 Marburg University, Department Biology , Molecular Plant Physiology, Karl-von-Frisch-Strasse 8, D-35043 Marburg, Germany 9 Center for Synthetic Microbiology (SYNMIKRO), Marburg University , Karl-von-Frisch-Strasse 14, D-35043 Marburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lars M Voll Daniela Elisabeth Nordzieke 1 Institute of Microbiology and Genetics, Genetics of Eukaryotic Microorganisms, University of Göttingen , GZMB, Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: dnordzi{at}gwdg.de Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Colletotrichum graminicola causes significant losses of the staple crop maize worldwide. The fungus produces two distinct asexual spore types, oval and falcate conidia, which show unique processes in development and plant interaction. Based on genome resequencing of our laboratory strain (CgM2/ M1.001), we investigated the gene expression profiles of oval and falcate conidia during development and the establishment of the biotrophic phase after leaf penetration using RNA-seq. Our results reveal specific gene expression profiles between the two spore types, indicating fundamental differences in their developmental programs that reflect different modes of infection. We identified spore type-specific expression patterns for genes encoding transcription factors, conserved fungal developmental genes, transporters, genes of secondary metabolite clusters, and pathogenicity-related functions, including effectors and carbohydrate-active enzymes (CAZymes). Our study shows that despite of the identical genomic basis, oval and falcate conidia have their own identity, and retain it in the process of germination, plant penetration, and biotrophy. Taken together, these results provide new insights into the molecular mechanisms underlying the infection process and have significant implications for understanding the biology of C. graminicola and its interaction with the plant host. Introduction Colletotrichum is a genus of phytopathogenic fungi within the Glomerellaceae family and poses a significant threat to global food security by causing devastating diseases in numerous crop species consumed by humans [ 1 ]. Colletotrichum species interact with a broad range of plant hosts, including cereals (maize, rice, sorghum), fruits (bananas, mango, avocado, citrus, strawberry), legumes (soybean, common bean), and vegetables, which makes it a major constraint in global food production [ 1 – 9 ]. Species of the Colletotrichum genus are grouped into 15 species complexes and exhibit a remarkable diversity in their infection strategies, ranging from biotrophs, necrotrophs, and hemibiotrophs to endophytes, allowing them to colonize and obtain nutrients from their hosts in various ways [ 6 , 10 ]. The genus has been ranked as the eighth most important group of plant pathogenic fungi globally, reflecting its significant scientific and economic impact [ 11 , 12 ]. One of the most important Colletotrichum species infecting cereals is C. graminicola , which primarily infects maize ( Zea mays ) and causes substantial losses worldwide [ 13 , 14 ]. Maize is among the world’s most extensively cultivated crops, playing an essential role in global food systems as a fundamental source of nutrition for humans and animals, and serving as a key raw material in various industrial applications [ 15 , 16 ]. C. graminicola can infect all plant parts and can be found throughout the growing season, making it a significant threat to maize production [ 17 ]. The disease manifests in anthracnose leaf blight (ALB), anthracnose stalk rot (ASR), and systemic plant infections, whereas current analyses estimate that ASR is the most important form causing 10-20% annual loss in maize harvest worldwide [ 14 ]. For efficient plant infection, C. graminicola produces two distinct spore types, oval and falcate conidia [ 18 , 19 ]. In contrast to other asexual spore type pairs from plant pathogenic fungi like Magnaporthe oryzae , oval and falcate conidia are both metabolically active, but are formed in distinct processes and locations of an infected maize plant [ 20 , 21 ]. Whereas falcate conidia develop from conidiophores in acervuli on infected leaves, oval conidia are formed from hyphae in parenchyma cells in both leaf and stem lesions [ 14 , 22 , 23 ]. Characterization of both conidia types revealed that they show significant differences in spore hydrophobicity, developmental processes and the adaptation to leaf or root infection [ 22 , 24 , 25 ]. Newly generated falcate conidia have a pronounced dormant stage, which is controlled by the secondary metabolites mycosporine-glutamine and mycosporine-alanine [ 22 , 26 ]. Either exposure to glucose or sensing of a hydrophobic surface, mimicking the plant leaf cuticle, induce a break of dormancy to give rise to either vegetative or pathogenic development, respectively [ 24 , 26 ]. In contrast, oval conidia lack a dormant stage and start to germinate even under nutrient limited conditions [ 22 ]. Oval conidia-derived germlings sense and grow towards each other by following chemical gradients to form a vegetative hyphal network by anastomosis fusions, a process that has never been observed for falcate conidia so far [ 22 , 27 ]. The early developmental processions of both conidia types have consequences for the infection strategies on leaves: falcate conidia form melanized appressoria from short germ tubes which will subsequently form penetration pegs and infect the subjacent plant material. Oval conidia, however, form penetration structures from hyphae (hyphopodia) only when conidia are present in high spore densities. Thus, falcate conidia cause more severe symptoms on leaves when applied in lower spore numbers [ 22 ]. In the research community, the C. graminicola isolate CgM2 (M1.001), which was collected in Missouri from infected maize, is used for scientific investigations due to the reliable symptom development on leaves and stalks [ 28 ]. The first genome sequence of C. graminicola M1.001/CgM2 was generated in a community effort in 2012 using Sanger and 454 sequencing platforms, with a resulting reference assembly spanning 50.9 Mb. Due to the high amount of repetitive sequences in the reference strains’ genome, the scaffolding was done based on the Colletotrichum higginsianum genome [ 29 ]. A follow up study combined deep sequencing RNA-seq data with sophisticated bioinformatics strategies to improve genome annotation, which included identification of several hundred novel transcripts, improved gene models, and revealed candidate genes for alternative splicing [ 30 ]. In 2023, a subsequent genome assembly of the same strain was generated using a combination of PacBio and Illumina sequencing technologies. Overall, 10 main chromosomes and 3 minichromosomes were identified, with a total genome size of 57.43 Mb, providing a high-quality genome sequence for this pathogen [ 17 ]. Those genomes were the bases of RNA-seq studies, focusing on different stages of leaf infection caused by falcate conidia. In those studies, genes encoding effectors, enzymes for secondary metabolites, transporters, hydrolases, and plant defence responses were identified as relevant for leaf infection by this spore type [ 29 , 31 , 32 ]. In this study, we investigated gene expression profiles of oval and falcate conidia during development and early leaf infection via RNA-seq, aiming to explain the determining factors shaping spore type-specific behaviour shown in previous work. To generate a sound basis for RNA-seq evaluation, we applied Nanopore and Illumina techniques to re-sequence the genome of our laboratory strain, which is present today in several lines around the globe, originating all from CgM2 (M1.001). We focused on the analysis of several functional gene groups relevant for development and pathogenicity, namely conserved fungal developmental genes and genes encoding for transcription factors, membrane transport proteins, secondary metabolite enzymes (encoded in gene clusters), effectors, and carbohydrate-active enzymes (CAZymes). Overall, we found significant differences in gene expression profiles comparing oval conidia, falcate conidia and mycelium, indicating fundamental biological differences even in freshly harvested spores. Furthermore, we identified genes that encode pathogenicity relevant functions such as effectors, CAZymes, and transporters; some of these functions show spore type-specific expression patterns. The reported results support the view that oval and falcate conidia-derived hyphae maintain their identity even after germination and induction of pathogenicity, shaping plant interaction in spore type-specific ways. Materials and Methods Strains, media and growth conditions The wild-type strain CgM2 (M1.001) of C. graminicola (Ces.) G.W.Wilson was provided by H. Deising (Martin-Luther University Halle-Wittenberg) and used in this study [ 28 , 29 ]. Falcate conidia were generated by growing C. graminicola on oat meal agar (OMA: 1l contains 50 g ground oat flakes (Alnatura, Darmstadt, Germany)) for 14-21 d. For the generation of oval conidia, mycelial plugs or a falcate conidia spore solution were inoculated in liquid complete medium with 0.5 M of sucrose (CMS, 1l: 55.5mM glucose, 0.1% yeast extract, 0.1% peptone, 0.5 M sucrose, 5.3 mM Ca(NO 3 ) 2 ) 0.073 mM KH 2 PO 4 , 1.04 mM MgSO 4 , 0.46 mM NaCl) and shaken (80 g, 23°C) for two days followed by 3–5 days incubation in darkness without agitation [23°C; 25]. Mycelium was grown in liquid CM medium (CM, 1l: 1% glucose, 0.1% yeast extract, 0.1% peptone, 5.3 mM Ca(NO 3 ) 2 ) 0.073 mM KH 2 PO 4 , 1.04 mM MgSO 4 , 0.46 mM NaCl) using either mycelial plugs or spore solutions of the CgM2 wild-type strain, followed by cultivation for 5 d at 23°C. Preparation of genomic DNA for genome resequencing Genomic DNA (gDNA) was prepared from mycelium grown for 5 d in liquid CM medium. To allow the sequencing of full chromosomes of CgM2, the NucleoBond ® HMW DNA kit (Macherey-Nagel, Düren, Germany; ref 740160.2) was used for high molecular weight gDNA extraction according to the manufacturer’s instructions. Concentration of the isolated DNA was determined using the Qubit ® dsDNA HS Assay Kit as recommended by the manufacturer (Life Technologies GmbH, Darmstadt, Germany). Shotgun libraries were prepared using the Illumina ® DNA Prep, (M) Tagmentation kit and Nextera™ DNA CD Indexe kit (96 Indexes) as recommended by the manufacturer (Illumina Inc., San Diego, CA, USA). To assess quality and size of the libraries, samples were run on an Agilent Bioanalyzer 2100 using an Agilent High Sensitivity DNA Kit as recommended by the manufacturer (Agilent Technologies, Waldbronn, Germany). Concentration of the libraries were determined using the Qubit ® dsDNA HS Assay Kit as recommended by the manufacturer (Life Technologies GmbH, Darmstadt, Germany). Sequencing was performed by using the NovaSeq6000 instrument (Illumina Inc., San Diego, CA, USA) using the NovaSeq6000 SP Reagent Kit (v1.5) and the NovaSeq XP 2-Lane Kit (v1.5) for sequencing in the paired-end mode 2×250 cycles. For Nanopore sequencing 1.2 µg HWD was used for library preparation using the Ligation Sequencing Kit 1D (SQK-LSK109) as recommended by the manufacturer. Sequencing was performed for 72 h using a MinION device Mk1B and a SpotON Flow Cell R9.4.1 as recommended by the manufacturer (Oxford Nanopore Technologies) using MinKNOW software (22.05.5) for sequencing and Guppy (v7.1.3) in high accuracy mode for basecalling. Preparation of RNA samples and RNA extraction procedures RNA of mycelium and freshly harvested falcate and oval conidia of wild-type CgM2 were isolated and prepared for subsequent sequencing. CgM2 falcate conidia were harvested after 28 d cultivation on OMA using a 0.02% Tween 20 solution. Oval conidia were generated in liquid CMS medium and separated from mycelium by filtration (Miracloth, EMD Milipore Corp, Burlington, USA; ref 475855-1R). Both spore types were centrifuged (4,000 g, 10 min), the supernatant discarded and the pellet collected on a sterile filter disc (grade 3hw, Sartorius, Göttingen, Germany; LOT 10-038) with the help of a vacuum pump (Type: PM12640-026.3, Nr. 02128309, Biometra, Germany). To access gene expression during spore germination and germling fusion, distinct harvesting time points were chosen to reflect different stages of oval or falcate conidia-specific developments. Oval conidia rapidly germinate also without the requirement of nutrient sources and reach levels of more than 80% germination after 6 h. Germling network formation by oval conidia starts at that time point, but reaches its maximum after about 17 h post inoculation [ 22 ]. Falcate conidia germination, however, is tightly controlled and several factors ensure spore dormancy [ 24 , 26 , 33 ]. We found that after 72 h inoculation on water agar, a low percentage of falcate conidia started to germinate, indicating dormancy breaking. To compare gene expression in those developmental processes in the two conidia types, we harvested cells at 5, 16, and 72 h after cultivation in 6 well plates (TC-Platte 6 Well Cell+,F; Sarstedt, Germany, ref 83.3920.300) filled with 500 µl 25 mM NaNO 3 solution. The wells were filled with pieces of miracloth (Miracloth, EMD Milipore Corp, Burlington, USA; ref 475855-1R), providing the required surface to induce germination and germling fusion. Subsequently, 450 μl HPLC H 2 O and 50 μl spore stock of falcate conidia (c = 21.4 × 10 6 ml -1 ) or oval conidia (c = 25.98 × 10 6 ml -1 ) was added to the 6 well plates. Here, the concentration was different based on the maximum germination and germling fusion rates observed. After incubation at 22°C, liquid and fungal tissue were sampled by pipetting on a filter disc (grade 3hw, Sartorius, Göttingen, Germany; LOT 10-038) with the help of a vacuum pump (Type: PM12640-026.3, Nr. 02128309, Biometra, Germany). The dried filter was frozen in liquid nitrogen until RNA was extracted. In a previous study, we found that oval and falcate conidia show a different behaviour prior to leaf infection [ 22 ]. Falcate conidia form appressoria from short germ tubes, followed by penetration of the plant surface. Oval conidia form networks by germling fusions when present in high spore densities. At the same time, hyphopodia, melanized penetration structures from hyphae, emerge and penetrate the plant surface [ 22 ]. Although germling fusion and hyphopodia formation are spore concentration-dependent processes, network formation is not a prerequisite for the development of hyphopodia [ 19 ]. To access which genes are regulating the spore type-specific leaf infection strategies, we prepared RNA from infected maize leaves (cultivar Mikado, KWS SAAT SE, Einbeck, Germany). Zea mays plants were grown in a 4:1 mix of soil (SP Vermehrung, Einheiterde Werkverband e.V., Sinntal-Altengronau, Germany, Artikel-Nr. 11-01500-40) and vermiculite (grain size 2–3 mm, Isola Vermiculite GmbH, Sprockhövel, Germany) in a controlled environment (PK 520 WLED plant chamber (Poly Klima Climatic Growth System, Freising, Germany), day/ night cycle of 12 h (26°C /18°C)) for 16 d. Secondary leaves of the grown plants were placed in square petri dishes (Petri Dish 100×100×20mm, Sarstedt, 16 Park Way, Mawson Lakes, South Australia 5095, REF 82.9923.422) on top of wet blotting paper (BF2 580 x 600 mm, Sartorius, Göttingen Germany). Conidia suspensions were prepared in 0.01% Tween solutions to ensure attachment to the leaf surface. Then 10-12 drops, each of 10 µl (c = 10 5 ml -1 ), were applied to the leaves. The square plates were sealed to ensure high humidity. After incubation for 24 h at 22°C, infection spots were collected (4mm Miltex Biopsy Punch with Plunger, Integra, Mansfield, USA; ref 33-34-P/25) in an Eppendorf tube and stored in liquid nitrogen until RNA extraction. RNA extraction was performed using a RNeasy Plant mini kit (Qiagen, Hilden, Germany, ref 74904). The eluted RNA was treated with DNase (40 μg) using Turbo DNA free kit (Invitrogen, Vilnius, Lithuania, ref AM1907) to remove DNA contamination. Clean up of the samples was performed using a RNeasy mini elute clean up kit (Qiagen, Hilden, Germany, ref 74204). Depending on the follow up application, RNA was either prepared for RNA sequencing or transcribed into complementary DNA (cDNA) as template for qRT-PCR (Supplementary figure 1). Validation of RNA-Seq Data by quantitative Real-Time PCR (qRT-PCR) Analysis For quantitative PCR, cDNA samples were prepared using the purified RNA samples and an iScript cDNA synthesis kit (BIO RAD, CAT- 1708891). RT qPCR was the performed using SsoAdvanced universal SYBR green supermix (BIO RAD, CAT-1725271). For detection, a CFX Connect Real Time PCR detection system (Bio-Rad Laboratories, Hercules, CA, USA) was used. The GAPDH gene was used as the internal reference (housekeeping gene). Relative expression levels of target genes were calculated using the ΔΔCt method [ 34 ], normalizing each sample to its corresponding GAPDH value. Fold change values (log₂ fold change ≤ –1 or ≥ 1, a value of −1 means that a gene is two-fold downregulated, whereas a value of 1 means that the gene is two-fold upregulated in this comparison; adjusted p-value < 0.1) were applied for comparative analysis. Heatmaps representing the expression profiles from both RNA-Seq and qRT-PCR datasets were generated using the iDEP.96 platform [ 35 ]. Genome assembly and annotation Oxford Nanopore reads of at least 10 kb were assembled with Canu v2.2 [ 36 ] with parameter genome size = 58 m. The resulting assembly was corrected with four rounds of Racon v1.4.3 [ 37 ] using the Oxford Nanopore reads and four rounds of Pilon v1.24 [ 38 ] using the trimmed Illumina reads. The resulting 17 contigs were compared with the previously published genome V1 including the published optical map data [ 29 ] to assign the contigs to chromosomes resulting in 13 contigs representing chromosomes. Racon and Pilon with the Nanopore and Illumina reads, respectively, were used to close the gaps introduced in the scaffolding. The resulting gapless contigs were analyzed for the presence of telomeric repeats (sequence TTAGGG) using a custom-made Perl script. Telomeric repeats were found at both ends of all contigs except for contig 10 that has a telomeric repeat at one end and 24 copies of the ribosomal RNA (rRNA) genes at the other end (most likely preventing assembly of telomeric repeats at that end). BUSCO (Benchmarking Universal Single-Copy Orthologs v.5.2.2) analysis [ 39 ] with BUSCO dataset fungi_odb10 showed 97.8 % completeness. For gene annotation, RNA-seq reads derived from the combined RNA of vegetative mycelium and two different spore types were assembled with Trinity v2.15.1 [ 40 ]. The resulting de novo assembled transcripts were used together with the predicted proteins from the published V1 genome [ 29 ] for genome annotation with MAKER (v3.01.03) [ 41 ] as well as for the BRAKER2 pipeline (v2.1.2) [ 42 – 47 ]. For the latter, two separate runs were conducted with protein and transcriptome input, respectively, and the resulting output files were combined using TSEBRA. The gene models from MAKER and BRAKER2 were combined using custom-made Perl scripts. Subsequently, another MAKER run was conducted using the predicted proteins and RNAs from the combined MAKER and BRAKER2 analyses together with predicted proteins from a recently published C. graminicola genome assembly [ 17 ]. BUSCO (Benchmarking Universal Single-Copy Orthologs v.5.2.2, [ 39 ] analysis of the resulting predicted proteins with the fungi_odb10 dataset showed 97.1 % completeness. Annotation of relevant gene groups Transcription factor encoding genes Data from the 312 predicted transcription factor genes identified previously in Neurospora crassa OR74A [ 48 ] was used in this study. The predicted genome of C. graminicola was aligned with these predicted TFs using local BLAST+ commant line homology searches. The matches of the discontigous megablast was further scrutinized by a BLASTN (BLAST+ 2.15.0 version) search run against the predicted genome in order to remove TF-unrelated as well as mispredicted sequences. The final list contains all the transcription factors with E value of <1e-05. Effector encoding genes Locus Tags of effector encoding genes were assigned based on previous effector gene identification performed by Becerra and coworkers [ 17 ]. Genes encoding membrane transporters Genes encoding membrane transport proteins were identified by a multilayered approach. Genes that had already been annotated in the genome version v4.0 [ 29 ] were identified by BLAST searches. The remaining genes were filtered by WoLF PSORT ( https://wolfpsort.hgc.jp/ ) for plasma membrane, ER, vacuolar or mitochondrial localization and after that, by PFAM domain predictions typical for transport proteins (e.g. MFS_1, Sugar_tr, AA_permease, etc.). In this way, 104 novel transporter genes were identified. These candidate genes were then annotated by BLAST (version 2.14.0) searches against Transporter Classification Database ( www.tcdb.org ), the Saccharomyces Genome Database ( www.yeastgenome.org ) and the NCBI Reference Sequences database ( www.ncbi.nlm.nih.gov ), as described by [ 29 ]. Genes encoding CAZymes The predicted proteome of C. graminicola was scanned against dbCAN3 v11 database [ 49 ] for CAZymes by using all tools (HMM, DIAMOND, dbCAN-sub) within the run_dbcan4 tool. Matches with an HMM e-value below 1e -15 and coverage greater than 0.35 were considered for further steps. Additionally, the proteome was screened by the CUPP online server [ 50 ] with default settings and considering matches with a significance score above 5. Matches identified exclusively by DIAMOND were excluded from further analyses. Protein classification was based on consistent matches across all used tools, while inconsistent matches were disregarded for subsequent CAZyme analyses. Substrate prediction was achieved by using the dbCAN_sub database [ 49 ]. Genes encoding regulators of development For identification of autophagy related proteins, proteins involved in signaling (MAP kinase pathways, NOX proteins, STRIPAK components, pheromone-signaling proteins), transcription factors involved in developmental processes and melanin biosynthesis proteins, a reciprocal BLASTP (version 2.0.10) and a BLASTN (BLAST+ 2.15.0 version) analysis was conducted based on Sordaria macrospora and Aspergillus flavus proteins sequences, respectively. The highest sequence identity and an e-value threshold <1e -05 and a continuous overlap of 50% over the query sequence was used for the detection of C. graminicola homologs. Secondary metabolite biosynthesis genes In order to locate secondary metabolite biosynthesis gene clusters, the genome was analyzed with antiSMASH version fungiSMASH 7.0.0 [ 51 ]. RNA-seq analysis RNA extraction was performed using a RNeasy Plant mini kit (Qiagen, Hilden, Germany, ref 74904). The eluted RNA was treated with DNase (40 μg) using Turbo DNA free kit (Invitrogen, Vilnius, Lithuania, ref AM1907) to remove DNA contamination. RNA was prepared for RNA sequencing by clean-up of the samples using a RNeasy mini elute clean up kit (Qiagen, Hilden, Germany, ref 74204). The NEB Next Poly(A) mRNA Magnetic Isolation Module (New England BioLabs, Frankfurt am Main, Germany) was used to reduce the amount of rRNA-derived sequences. For sequencing, the strand-specific cDNA libraries were constructed with a NEB Next Ultra II Directional RNA library preparation kit for Illumina and the NEB Next Multiplex Oligos for Illumina (96) (New England BioLabs, Frankfurt am Main, Germany). To assess quality and size of the libraries samples were run on an Agilent Bioanalyzer 2100 using an Agilent High Sensitivity DNA Kit as recommended by the manufacturer (Agilent Technologies). Concentration of the libraries was determined using the Qubit® dsDNA HS Assay Kit as recommended by the manufacturer (Life Technologies GmbH, Darmstadt, Germany). Sequencing was performed on the NovaSeq 6000 instrument (Illumina Inc., San Diego, CA, USA) using NovaSeq 6000 SP Reagent Kit (100 cycles) and the NovaSeq XP 2-Lane Kit v1.5 for sequencing in the paired-end mode and running 2x 61 cycles. RNA from 12 different samples with biologically independent triplicates per sample was analyzed by paired-end Illumina sequencing (Supplementary table 1). Reads were mapped to the genome sequence with Hisat2 v2.2.1 [ 52 ]. Reads mapping to annotated features were counted as described by [ 53 ] with the modification that reads were strand-specific and counted if they mapped to the corresponding strand of the feature. Quantitative analysis of gene expression was done in R (v4.1.2) [ 54 ] with DESeq2 1.34.0 [ 55 ]. Genes were identified as differentially expressed if they had an adjusted P-value (Padj) of <0.1 and log2 values of fold expression changes were either ≥1 for upregulated or ≤1 for downregulated genes. Results Genome resequencing of the C. graminicola laboratory strain CgM2 (M1.001) Since its collection, the CgM2/M1.001 isolate is used in several labs around the world. We therefore performed resequencing of our laboratory strain in this study to generate a reliable basis for data evaluation and interpretation. In this genome version V5, we were obtained a gapless assembly with telomeric repeats at both ends of most chromosomes with the exception of chromosome 10, which contains the rDNA repeats. In total, we assembled 13 chromosomes for C. graminicola , including the minichromosomes Chr11, Chr12 and Chr13. All chromosomes sum up to 57,600,233 base pairs in total. Using RNA-seq of a mixed sample containing biological material of C. graminicola mycelium, oval, and falcate conidia, 15,481 protein-encoding genes were annotated (Supplementary file 1). Although chromosome 1 is the longest, spanning 7,645,418 base pairs with 2,013 predicted genes, chromosome 2 contains the highest number of predicted genes (2,289). In total, our genome version V5 is very similar to V4 [ 17 ], however, the total number of encoded proteins in V5 is increased ( Table 1 , Figure 1 ). Download figure Open in new tab Figure 1: Overview of the C. graminicola assembly V5 (A) Size of different chromosome in base pair (bp). (B) Annotation of different functional gene groups based on the number of genes on 13 chromosomes, colored bars represent different functional categories. The black bar represents the total number of genes on one chromosome, including colored functionally categorized genes. View this table: View inline View popup Download powerpoint Table 1: Comparison of assembly statistics for the C. graminicola M1.001 genome. Differential gene expression dynamics during spore development and maize leaf infection In the 1970s, it was discovered that C. graminicola generates two distinct spore types [ 56 ]. Recent investigations from our lab provided evidence that the differences cumulate in specialization of the two spore types for the infection of different plant tissues [ 22 , 25 ]. In this study, we performed a comparative RNA-seq analysis to unravel the genetic basis for the observed biological differences between the spore types, focusing on three relevant processes identified in earlier work: germination, germling fusion and early leaf infection. We included samples of freshly harvested oval and falcate conidia as well as mycelium of C. graminicola as controls. Based on the RNA-seq data, we performed a Principal Component Analysis (PCA) to access the quality of the generated data, and to visualize sample clustering and variation among the biological replicates. In our study, PC1 explains 55% of the total variance, while PC2 accounts for an additional 15%, indicating that these two components together capture a substantial 70% of the transcriptomic variation ( Figure 2A ). Biological replicates of the different samples clearly cluster according to spore type (falcate and oval conidia), germination stage (5h–16h, 72h), leaf infection (1dpi) with mycelium as an outgroup. Notably, falcate conidia (Fc) and oval conidia (Oc) samples are separated by PC1, demonstrated strong transcriptional differences between the two spore types. Within each group, non-germinated spores and early germination stages (5h, 16h) separate clearly, suggesting stage-specific expression changes. Mycelium samples (Myc) cluster apart from all conidial stages, emphasizing a specific expression profile during vegetative growth. The separation of leaf infection samples (Fc1dLF, Oc1dLF) from other samples predominantly by PC2 further supports the presence of transcriptional reprogramming during host-pathogen interaction. Download figure Open in new tab Figure 2: Differentially expressed genes at different developmental stages in two different conidia types identified by DESeq2. (A) Principal component analysis of RNA-seq data for all samples. The first two principal components explain 70 % of variance. The four major groups of samples consist of the mycelium samples, the oval conidia (w/o the infection samples), the falcate conidia (w/o the infection samples), and the leaf infection samples. The Venn diagrams present an overview of differentially regulated genes of (A) freshly harvested falcate and oval conidia, (B, C) developmental stages of oval conidia (B) and falcate conidia (C). Visualization of the overall up and down-regulated genes in Venn Diagrams was used to explore differential gene expression during spore development and leaf infection ( Figure 2B ). In freshly harvested oval and falcate conidia, >1000 genes were differentially regulated relative to vegetative mycelium in a spore type-specific manner, underpinning the different nature of those conidia types compared to each other and also to vegetative mycelium. Furthermore, we observed differences in gene expression during developmental transitions, including germination (5 h), germling fusion (16 h, 72 h), and early leaf infection (1 dpi), between the two conidia types. In falcate conidia, 771 genes were up-regulated and 940 down-regulated across all developmental stages and during early leaf infection. Oval conidia displayed even stronger transcriptional dynamics, with 1,238 genes up-regulated and 1,054 down-regulated, reflecting a more pronounced transcriptional reprogramming associated with developmental progression and pathogenesis. To obtain additional insight, we examined the 100 most differentially expressed genes across developmental transitions, including freshly harvested conidia (control), early germination (5 h), germling fusion (16 h), and maize leaf infection (1 dpi, Supplementary figures 2-5). Pathway enrichment analysis performed with iDEP.96 revealed distinct expression signatures of freshly harvested oval and falcate conidia and early infection stages (Supplementary table 2). Specifically, genes involved in rRNA processing, cellular responses to DNA damage, and general stress responses were predominantly up-regulated in freshly harvested conidia, reflecting transcriptional programs associated with dormancy and survival. In contrast, during early leaf infection (1 dpi), there was significant enrichment for pathways linked to secondary metabolite biosynthesis, hemicellulose metabolism, and hydrolase activity, consistent with the activation of plant cell wall degradation and pathogenicity-related functions. Validation of differential gene expression by qRT-PCR To confirm the transcriptomic data obtained from RNA-seq (Supplementary file 2), a subset of differentially expressed genes was validated using quantitative real-time PCR (qRT-PCR, Figure 3 , Supplementary table 3). The selection was based on the significance of the RNA-seq data (top 20 up- and down-regulated genes) and interesting regulation patterns dependent on spore type, developmental stage or early infection processes. Among the selected seven genes, COGRA5_13220 (Emopamil binding protein), COGRA5_06545 (Cutinase), COGRA5_00514 (Apoplastic effector) and COGRA5_09579 (Fungal RiPP-like secondary metabolite) showed interesting expression patterns, indicating a probable role of these in specific developmental transitions, such as dormancy (COGRA5_13220), dormancy breaking of falcate conidia (COGRA5_06545), germination induction of oval conidia (COGRA5_00514), and adhesion (COGRA5_09579). Regarding early leaf infection, we selected COGRA5_00442 (Carboxylesterase), which is overexpressed in falcate conidia during maize leaf infection, while COGRA5_14555 (Taurine catabolism dioxygenase TauD) showed increased expression in oval conidia under the same condition (Supplementary file 3). In contrast, COGRA5_09579 (Fungal RiPP-like secondary metabolite) is up-regulated in both spore types during infection, indicating shared pathogenicity-related functions. Overall, expression profiles obtained from qRT-PCR (Supplementary figure 6) were similar to the RNA-seq results and thus confirmed the robustness of the RNA-seq findings. Download figure Open in new tab Figure 3: Comparative analysis of transcriptomic expression using both qRT-PCR and RNA-seq of selected genes. The data reflect the fold change correlations between these two methods, presented as a heat map for selected genes with a potential role in the distinct developmental stages of falcate and oval conidia (log2 fold change =1, padj <0.1). The heatmap was created using the iDEP.96 [ 35 ]. Red color depicts the up-regulated genes, whereas blue represents the down-regulated genes. Annotation and transcriptome profiling of oval and falcate conidia across different functional gene groups To gain an overview of the encoded genes, we performed a comprehensive annotation of all predicted genes with respect to their putative roles in fungal development (conserved regulators of development, transcription factors, secondary metabolites) and pathogenicity (carbohydrate-active enzymes (CAZymes), effectors, nutrient transporters) ( Figure 1B ). All analyzed functional gene groups are broadly distributed across all ten major chromosomes (Chr 1–10). In contrast, the microchromosomes (MCs) in C. graminicola revealed that they are enriched in repeats and have reduced gene content compared to the core chromosomes. The MCs harbor a limited number of annotated genes, with approximately 84% of the total 160 genes being hypothetical. Interestingly, some of the annotated genes on the MCs are related to pathogenicity, including COGRA5_15372 (minichromosome 11), which encodes glutathionylspermidine synthase and has been previously shown to contribute to virulence in C. graminicola [ 57 ]. Our RNA-seq data analysis also revealed strong induction of COGRA5_15372 during early leaf infection (1dpi) by falcate conidia, suggesting a role in plant penetration and establishment of infection. Furthermore, the minichromosome 12 and 13 harbor 4 ankyrin repeat proteins, which are versatile protein motifs involved in mediating diverse protein-protein interactions [ 58 , 59 ]. In fungi, ankyrin repeat proteins have been shown to function as transcription factors or regulators of secondary metabolite biosynthesis, including toxins that contribute to virulence [ 60 ]. 1. Transcription factor encoding genes Transcription factors (TFs) are proteins that bind to specific DNA sequences and regulate gene expression. All eukaryotes, including fungi, depend on appropriately orchestrated TFs to coordinate the expression of genes essential for individual developmental stages and for the induction of vital metabolic pathways. These pathways encompass carbohydrate, iron, and nitrogen metabolism, as well as tolerance to oxidative stress, osmotic conditions, pH levels and UV light. Furthermore, TFs are instrumental in establishing developmental processes like vegetative growth, tissue differentiation (fruiting bodies, conidiophores), and pathogenicity [ 61 ]. Analysis of the C. graminicola genome revealed 216 identified transcription factors (TFs) representing a diverse array of gene families (Supplementary file 4). The Zn 2 C 6 (GAL4-like zinc cluster) and C 2 H 2 zinc finger families were the most prevalent, consistent with their known abundance and regulatory significance in plant-pathogenic ascomycetes. To elucidate the functional dynamics of the identified TF genes, we performed differential expression analysis across different developmental stages and during maize leaf infection (1 dpi) in the two distinct conidial morphotypes ( Figure 4A ). This analysis showed that a majority of TF genes exhibited higher expression in freshly harvested conidia, with a general trend towards down-regulation during subsequent development and maize leaf infection. Investigation of the 100 most differentially expressed TF genes using iDEP.96 highlighted several genes that showed opposite regulation patterns in oval or falcate conidia over the different analyzed stages: in freshly harvested falcate conidia, specific TFs are down-regulated, but expression increases in all developmental and pathogenicity stages. For oval conidia samples, we observe the opposite: up-regulation in freshly harvested spores and down-regulation in later stages ( Figure 4B ). Notable examples include COGRA5_02342 (ascospore maturation protein; KilA-N domain), COGRA5_07325 (Zn cluster transcription factor; Zinc finger), and COGRA5_03862 (homeobox domain-containing protein; Homeobox domain). In contrast, for TFs up-regulated during oval conidia development, we did not find such clear differences to falcate conidia (Supplementary Figure 7). Download figure Open in new tab Figure 4: Differential expression of transcription factors in relation to spore type and developmental stages. (A) Total number of predicted transcription factor genes showing differential regulation. (B) Transcription factor genes with induced expression for falcate conidia, as compared to oval conidia, among top 100 most variable genes. (C, D) Transcription factor genes (C) upregulated and (D) downregulated in both spore types during developmental stages. The top 100 variable genes were identified using iDEP.96. Hierarchical clustering was performed based on correlation distance with average linkage. Z-score normalization (centered by subtracting the mean) was applied, and a cut-off Z-score of 4 was used for visualization. Additionally, several TFs exhibited similar regulation patterns in both conidial types across developmental stages and leaf infection (1 dpi), such as COGRA5_05064 (transcriptional regulatory protein Pro1, C6 zinc finger) and COGRA5_05893 (nuclear division 74 protein; C2H2 zinc finger), were consistently up-regulated during development and leaf infection ( Figure 4C ). Conversely, others including COGRA5_14806 (Myb-like DNA binding protein; Myb domain) and COGRA5_10249 (female and male fertility protein; MATA_HMG box) were down-regulated ( Figure 4D ) in both conidia types. 2. Effector encoding genes Fungal effectors are secreted proteins or small molecules that facilitate host colonization by modulating plant immune responses. These molecules are differentially expressed based on host tissue in contact and stages of disease development in plant pathogenic species [ 62 ]. Depending on their mode of action and subcellular localization, effectors can be broadly categorized as apoplastic (extracellular) or cytoplasmic (intracellular) [ 63 ]. Apoplastic effectors act in the extracellular space of plant tissues, e.g. by binding to fungal cell wall components such as chitin to prevent host recognition, whereas cytoplasmic effectors are delivered inside plant cells, where they interfere with intracellular immune signaling and defense mechanisms. Based on comparison of annotated effector encoding genes of genome version 4 [ 17 ], 513 putative effector genes were identified, comprising 295 apoplastic, 159 cytoplasmic and 59 apoplastic\cytoplasmic candidates (Supplementary file 5). Analysis of the 100 most variable identified effector-encoding genes (Supplementary figure 8) identified several with higher expression in either falcate or oval conidia during the early stages of maize leaf infection (Supplementary figure 9). Interestingly, some effectors exhibited opposing patterns of regulation between spore types during infection ( Figure 5 , Table 2 ). For example, the known apopolastic effector COGRA5_04185 encoding for a hydrophobin [ 64 , 65 ] is strongly induced during leaf infection by falcate conidia, but is not regulated in leaves infected by oval conidia. In addition, several genes were strongly induced during falcate conidia infections, including effectors associated with necrotrophy. Notably, COGRA5_10559, previously described as a switch-specific effector [ 31 , 66 – 68 ], was significantly up-regulated when comparing falcate and oval conidia infections. Download figure Open in new tab Figure 5: Volcano plot of top 100 differentially regulated effector genes during early leaf infection by oval and falcate conidia. The volcano plot was generated based on the comparison of gene expression of falcate conidia leaf infection vs oval conidia leaf infection. VolcaNoseR settings were as followed: fold change threshold: −2 to 2; significant threshold: 2; use thresholds to annotate: changed and significant; criterion for ranking hits: Manhattan distance. Significantly regulated genes comparing falcate conidia leaf infection and oval conidia leaf infection are indicated in red (upregulated) and blue (downregulated). Locus tag numbers of the 10 most relevant hits are indicated. View this table: View inline View popup Download powerpoint Table 2: Effector genes showing the highest spore-type dependent specificity during during appressoria formation of C. graminicola . Evaluation based on the Top 100 regulated effector genes (supplementary figure 8). Genes were included when the log2Fold Change was <2 comparing early leaf infection samples of falcate with early leaf infection samples of oval conidia (LF_vs_LO). Genes with opposite gene regulations in oval or falcate conidia samples are highlighted in bold letters. 3. Genes encoding membrane transporters Transporters facilitate selective movement (import or export) of molecules across membranes. Organic and inorganic nutrients like sugars, amino acids, ions, or other metabolites and water across a likely to be taken up across the plasma membrane [ 70 ]. Transporters play a crucial role in securing survival, growth, morphogenesis, and pathogenesis by enabling the pathogen to adapt to different nutritional conditions in the host environment during the infection process [ 71 , 72 ]. The differential expression of transporter encoding genes can therefore indicate metabolic shifts in the host, transition of fungal developmental stages, and spore type-specific physiological strategies. From the total of 738 predicted transporter encoding genes in C. graminicola , including 104 newly annotated transporter genes, we analyzed the top 40 genes with the most variable expression profiles during spore development and early leaf infection in both falcate and oval conidia (Supplementary file 6, Figure 6 ). Among those, several transporter families, including drug: H + antiporter-1 (DHA1), sugar porters (SP), and anion:cation symporters (ACS), show elevated expression in both spore types, but transporters from the DHA1 and DHA2 families are much more frequently induced in falcate than in oval conidia, indicating increased challenge by host secondary metabolites to falcate conidia. Also in other respect, the profile of induced transporters differs strongly between oval and falcate conidia. 48% of the top 40 induced transporter-encoding genes in oval conidia were anion:cation symporters (ACS), whereas the same transporter family sums up to 20% in falcate conidia, indicating a spore type-specific strategy for the uptake of carboxylates such as succinate and citrate, organic acids and vitamins [ 73 ]. We further identified several other transporter gene families that show clear spore type-specific expression patterns, such as monocarboxylate porter (MCP), p-ATPases, and tricarboxylate carriers, which are prominently up-regulated in falcate conidia, indicating adaptation to a nutrient environment rich in carboxylic acids (like C4 leaves). In oval conidia, several members from the oligopeptide transporter (OPT) and L-type amino acid transporter (LAT) families were induced. Since these were absent among induced transporter genes in falcate conidia, pointing to a probable adaptation to an amino acid and protein rich niche – or preference for these substrates. Download figure Open in new tab Figure 6: Induced transporter genes. Transporter gene families with elevated expression in falcate conidia and oval conidia, identified among the top 40 most variable genes by iDEP.96. Genes annotated as others are unannotated genes (hypothetical proteins). 4. Genes encoding CAZymes Microorganisms produce a diverse repertoire of carbohydrate-active enzymes (CAZymes) involved in the synthesis, modification, and degradation of complex carbohydrates [ 74 ]. These enzymes hydrolyze major plant cell wall polymers such as cellulose, hemicellulose, and pectin, enabling the utilization of plant biomass as a nutrient source. CAZymes are organized into distinct families based on protein sequence similarity and conserved three-dimensional structural folds [ 75 ]. RNA-seq analysis identified 633 putative CAZymes that were differentially regulated during C. graminicola development and pathogenicity (Supplementary file 7). This is a significant increase from the previously annotated 467 CAZymes, highlighting the complexity and diversity of CAZyme regulation in this fungus [ 76 ]. Several CAZyme families, and their corresponding substrate specificities, were specifically up-regulated in falcate and oval conidia at different developmental stages ( Figure 7 A, B). Some families showed strict spore type specificity, for example, Carbohydrate Esterase family CE16 (acetylesterase) was uniquely up-regulated in falcate conidia, whereas Auxiliary Activity famiy AA12+AA8 (oxidoreductase) was specifically induced in oval conidia. The most striking distinction between spore types was observed in the Auxiliary Activities (AA) family ( Figure 8 ). In falcate conidia, members of the AA9 family (lytic polysaccharide monooxygenases with cellulase and xylanase activities) were strongly up-regulated, alongside higher expression of Glycoside Hydrolase family GH10 cellulase/xylanase genes (Supplementary Figure 10). This suggests that falcate spores are primed for the rapid breakdown of cellulose- and hemicellulose-rich plant cell walls, facilitating host tissue penetration and enabling nutrient release during the early necrotrophic phase of infection. In contrast, oval conidia exhibited elevated expression of Polysaccharide Lyase family PL3 (pectate lyase) during early developmental stages (5 h and 16 h; Supplementary Figure 10). A specialization for the degradation of pectin, an abundant component of the middle lamella, could support more targeted and localized modification of host cell walls by oval conidia, potentially aiding in adhesion, subtle wall loosening, or early biotrophic establishment before extensive tissue damage is initiated. Download figure Open in new tab Figure 7: Gene expression profiles of Carbohydrate-Active Enzymes (CAZymes) in C. graminicola . (A) Distribution of CAZyme families and (B) corresponding predicted CAZyme substrate categories associated, that are upregulated in oval and falcate conidia. Download figure Open in new tab Figure 8: Expression dynamics (log ₂ fold change) of major CAZyme classes across different developmental stages . Germination (5 h), germling fusion (16 h), and maize leaf infection (1 dpi) were analyzed in transcriptome data obtained from falcate and oval conidia. Statistical significance indicates differences between conidial types within each CAZyme class at the corresponding developmental stage. 4.1 Genes encoding Carbohydrate-binding modules Carbohydrate-binding modules (CBMs) are a large group of protein domains that are commonly found attached to glycosyl hydrolase (GH) enzymes [ 77 , 78 ]. These modules are characterized by a relatively small number of amino acids, typically ranging from 30 to 200 amino acids in length [ 77 – 79 ]. CBMs lack catalytic activity, but instead function as substrate-binding modules, facilitating the interaction between the enzyme and its substrate [ 77 ]. CBM50 is one of the family of carbohydrate-binding modules (CBMs), also known as LysM domains. These modules are approximately 50 amino acids long and specifically bind to N-acetylglucosamine-containing carbohydrates such as chitin and bacterial peptidoglycans [ 29 ]. Our analysis revealed that the C. graminicola genome encodes 17 proteins with carbohydrate-binding modules (CBM50), which represents an increase of 3 proteins compared to the previously annotated 14 proteins [ 29 ]. Furthermore, transcriptome profiling showed that two of these CBM50 proteins, GLRG_02947 and GLRG_06565, are strongly upregulated in planta after 1 day post-infection [ 29 ]. Notably, GLRG_02947, which was previously predicted to be induced during leaf infection, is indeed highly induced in oval conidia leaf infection. In contrast, GLRG_06565, which was previously found to be poorly expressed at all stages of infection, is highly induced during falcate conidia leaf infection (Supplementary file 8). These findings suggest that these CBM50 proteins play a role in the infection process of C. graminicola . 5. Genes encoding regulators of development In fungi, the regulatory proteins coordinating crucial processes such as growth, spore formation (conidiogenesis), morphological differentiation, reproduction, and development of specialized structures like fruiting bodies and plant surface penetrating structures, are highly conserved [ 22 , 80 – 83 ]. For this study, we verified the presence of 122 conserved regulators of development using BLAST analysis based on Sordaria macrospora and Aspergillus flavus NRRL3357 protein sequences (Supplementary file 9 [ 84 , 85 ]. Differential expression profiling revealed that these candidate developmental genes exhibit distinct regulatory patterns across various developmental stages and during host leaf infection ( Figure 9 ). Download figure Open in new tab Figure 9: Heatmap of selected developmental genes differentially expressed in oval and falcate conidia during different developmental stages. Differential expression was determined with a log₂ fold change ≤ –1 or ≥ 1 and an adjusted p-value (padj) < 0.1. The heatmap was generated using iDEP.96, with red indicating upregulated genes and blue indicating downregulated genes. Among the major observations, melanin biosynthesis genes were up-regulated in both spore types during leaf infection. This aligns with previous findings that melanin functions are an important virulence factor in plant-pathogenic fungi, facilitating early host penetration, stress adaptation, and evasion of plant defenses [ 86 , 87 ]. We also observed induction of Rho4-like GTPase and NADPH oxidase (NOX) complex genes ( noxA , noxB , noxD ) during development and leaf infection by both spore types in C. graminicola . The small Rho GTPase-coding gene RHO4 in C. graminicola plays a crucial role in various cellular processes, including β-1,3-glucan synthesis, cell wall integrity, growth of vegetative hyphae, conidiation, infection structure differentiation, and is also required for full virulence [ 88 ], whereas NOX enzymes generate reactive oxygen species (ROS), which act as signaling molecules regulating fungal growth, morphogenesis, and pathogenicity [ 89 , 90 ]. In addition, genes associated with the MAP kinase cell wall integrity (CWI) pathway were up-regulated during development of both spore types, indicating an active need for cell wall remodeling and stress adaptation. These pathways are important for shaping fungal morphology, survival, and successful developmental transitions [ 91 – 93 ]. Another developmental gene group of interest was carbonic anhydrases (CA), which are involved in CO₂ sensing by generating bicarbonate that activates adenylyl cyclase. In our dataset, CA genes were up-regulated in both spore types during spore development and leaf infection [ 94 – 96 ]. Interference with CA activity can impair fungal growth and virulence, as CA inhibition disrupts the CO₂/HCO₃⁻ balance, a mechanism also described in bacteria [ 97 ]. 6. Secondary metabolite biosynthesis genes Secondary metabolites (SMs) are low molecular weight organic compounds produced by fungi, plants and bacteria. In contrast to primary metabolites, which are essential for the organisms’ survival, secondary metabolites are often only produced in specific environmental and developmental conditions [ 98 , 99 ]. These molecules are synthesized from primary metabolites and central metabolic pathways, with Acyl-CoAs molecules and amino acids serving as essential building blocks, depending on the way of biosynthesis [ 99 , 100 ]. Fungal SMs are chemically diverse and fall into four major classes: polyketides, terpenoids, phenolics, and non-ribosomal peptides [ 101 ]. Genes responsible for SM biosynthesis are typically organized in contiguous order as biosynthetic gene clusters [BGC, 100]. Regulation of BGCs is closely related to the ecological context and infection stage, underlining the adaptive significance of secondary metabolism during pathogenesis [ 100 , 102 ]. Based on the C. graminicola genome sequence V5, gene candidates belonging to SM biosynthesis clusters were predicted using antiSMASH version fungiSMASH 7.0.0 (Blin et al 2021). Among these, 1,375 genes are differentially regulated during various developmental stages and early stages of maize leaf infection 1 dpi (Supplementary file 10). Notably, 112 SM BGCs displayed a decreased expression in freshly harvested samples of both falcate and oval conidia. Conversely, several BGCs exhibited induction during leaf infection in both spore types ( Figure 10 ). Notably, the fusaridione A cluster, which was originally identified in Fusarium heterosporumin, was induced upon host interaction, aligning with previous reports [ 103 ]. A similar pattern is displayed by the depudecin BGC. This cluster enables biosynthesis of the histone deacetylase inhibitor depudecin, which contributes to virulence in Alternaria brassicicola [ 104 , 105 ]. Additionally, the BGC of three antimitotic mycotoxins, phomopsins A, B, and E, isolated first from the pathogenic ascomycetes Phomopsis leptostromiformis [ 106 – 108 ], display increased expression during host interaction. Download figure Open in new tab Figure 10: Heatmap highlighting known secondary metabolite gene clusters associated with the biosynthesis of fusaridione A, depudecin, and phomopsins (A, B, E), which are induced during leaf infection (1dpi) in both spore types. Depicted clusters had several genes identified among the top 100 most variable genes predicted by iDEP.96 (log2 fold change =1, padj <0.1). To get better overview of these clusters all the genes of that secondary metabolite gene clusters are shown in the figure. Red color depicts the up-regulated genes, whereas blue represents the down-regulated genes. Discussion In this study, we performed a comprehensive RNA-seq analysis based on genome re-sequencing of our laboratory strain of the corn anthracnose fungus C. graminicola CgM2/M1.001. We focused on the elucidation of spore type-specific gene expression patterns of critical stages in development and early leaf infection of oval and falcate conidia and their derived hyphae. For data interpretation, we concentrated on the most regulated genes of six functional gene groups, comprising genes relevant as transcription factors, effectors, transporter proteins, CAZymes, developmental regulators, and as parts of secondary metabolite gene clusters. Overall, we found gene expression changes of both conidia types in regard to germination and germling stages, along with a general increase in gene expression levels observed during the establishment of the biotrophic plant interaction. Specifically, we detected different gene expression patterns of oval or falcate conidia infected leaves regarding effector gene induction, the CAZyme repertoire and membrane transporters during early leaf infection. These findings point to a probable spore type-specific interaction with the plant immune system and dedicated nutrient acquisition strategies. Altogether, this study provides evidence for fundamental differences in gene expression for oval and falcate conidia gene that may form the basis for spore type-specific development and virulence of C. graminicola . In previous studies, genome sequencing in combination with RNA-seq was used to understand critical factors determining the symptoms developed by closely related plant pathogens. For example, in Zymoseptoria , a genus of plant pathogenic fungi specialized on the infection of grasses, comparison of the genomes of four different species identified species-specific orphan genes and genes encoding small, secreted proteins with putative functions for virulence and host specificity [ 109 ]. Furthermore, RNA-seq analysis of eight Fusarium fujikuroi isolates revealed a correlation between the formation of secondary metabolites and two distinct pathotypes on rice. In this case, gibberellic acid (GA) biosynthesis is positively correlated with typical symptom development of bakanae disease like hyper-elongated seedlings, while fumosin-generating strains showed stunting and early withering of infected seedlings [ 110 ]. Similarly, RNA-seq can give important hints to the regulation of plant defense responses during pathogenic interactions. For example, comparison of gene expression profiles of soybean infected with non-pathogenic and pathogenic strains of Fusarium oxysporum revealed high numbers and magnitudes of differentially expressed genes (HDEGs) in the plant comparing both infection scenarios [ 111 ]. In all those examples, different isolates of the same pathogenic species or closely related species were analyzed, having isolate-specific or species-specific genetic information encoded in their genomes, respectively. Whereas comparable studies are numerous, little information exists about the expression profiles of asexual spore types of a single species with the same genetic information. In one case, the transcriptomes of freshly harvested microconidia and macroconidia of the rice blast fungus Magnaporthe oryzae were analyzed, providing evidence that only seven genes are specifically expressed in microconidia and eight additional genes show increased expression in macroconidia [ 20 ]. In our study, the numbers of differentially regulated genes identified in freshly harvested oval and falcate conidia of C. graminicola were much higher ( Figure 2 ): compared to mycelium samples, oval and falcate conidia showed increased expression of 1651 genes in common and, additionally, similar numbers of upregulated spore type-specific genes (oval conidia: 1449, falcate conidia: 1874). We observed a similar pattern when comparing downregulated genes respective to mycelium samples: in both conidia types, 1884 genes are downregulated, but we observed >1000 genes specifically for oval (1213) and falcate conidia (1474). Having in mind that the genome of C. graminicola encodes 15,481 genes, we observed roughly 32% of the genes upregulated and 30% genes downregulated in freshly harvested oval and falcate conidia, underpinning the metabolic activity of both spore types and there per se differences. Monitoring later stages of germination, we observed rapid transcriptional changes in both spore types, although only oval conidia showed germination under the conditions monitored and falcate conidia remained dormant. These surprising observations, however, are in line with a previous report regarding transcriptional activity of dormant Apergillus sp. conidia, providing evidence that fungal conidia are able to react to environmental signals, shaping the transcripts and thus the later behavior after dormancy breaking [ 112 ]. Apart from general gene expression changes, we identified numerous genes with similar, specific or even opposite regulation in oval and falcate conidia, of which we further analyzed a subset using qRT-PCR ( Figure 3 ). For instance, COGRA5_11040, encoding an Aspzincin_M35 metallopeptidase, is highly induced in both oval and falcate conidia while the biotrophic plant interaction is established. Since metalloproteinases degrade proteins in host tissues and increase thereby the hosts’ susceptibility to disease, this result might indicate that oval and falcate conidia use the same strategy for masking their presence [ 113 , 114 ]. In contrast, Lysine-domain containing proteins (LysMs) were mainly induced during leaf infection with falcate conidia, but to a lesser extent with oval conidia (Supplementary file 8). However, the LysM encoding gene COGRA5_04487 even shows an opposite regulation. Fungal LysM proteins are effectors and play crucial roles in regulating development, maintaining cell wall integrity by binding chitin, and contributing to pathogenicity by suppressing host defenses and facilitating host colonization [ 115 , 116 ]. In contrast to those findings, most of the TauD genes present in C. graminicola genome are highly expressed during oval conidia leaf infection, but not in falcate conidia ( Figure 3 ; Supplementary file 3). TauD proteins enable fungi to survive in nutrient-deprived conditions by utilizing taurine and xanthine as nutrient sources [ 117 , 118 ]. This is consistent with our previous results that oval conidia are able to survive and germinate in nutrient-deprived conditions, but falcate conidia are not [ 22 ]. Intriguingly, nutrient starvation induced by cultivation media or the inoculation on intact plant surfaces induce fusion of oval conidia derived germlings, which give rise to hyphopodia formation for plant penetration [ 22 ]. TauD genes therefore could contribute to successful germination and germling fusion during colony formation and the pathogenicity of C. graminicola . Additionally, our RNA-seq analysis indicated that the two spore types of C. graminicola play different, complementary roles in maize plant infection. Falcate conidia are molecularly equipped for an aggressive enzymatic attack on host tissues, consistent with a necrotrophic infection strategy. This is evident from the strong induction of CAZyme families such as AA9 and GH10, which enable rapid breakdown of cellulose- and hemicellulose rich plant cell walls, facilitating tissue penetration and nutrient release early in infection together with an increased capacity for carboxylate uptake [119; Supplementary figure 10]. At the same time, the increased number of DHA transporters in falcate conidia points to an elevated capacity for the detoxification of host defense metabolites by this spore type [ 120 ]. In the secondary metabolite group, fumonisin B1, a well-known mycotoxin from Fusarium verticillioides that infects maize and other crops, was highly and specifically induced during falcate conidia mediated leaf infection [ 121 ]. Several effector genes were also strongly upregulated, including the previously characterized necrotrophy associated switch-specific effector COGRA5_10559, which was characterized in different hemibiotrophic fungal plant pathogens including C. graminicola [ 31 , 66 – 68 ], and newly identified, uncharacterized effector candidate genes COGRA5_06182 and COGRA5_08575 that may contribute to pathogenicity ( Table 2 , Figure 5 ). In contrast, oval conidia appear adapted for nutrient and sugar acquisition, suggesting a probable predominant biotrophic plant interaction. These spores express oligopeptide transporters (OPT) and L-type amino acid transporters (LAT), which aid nutrient uptake from the host [122; Figure 6, 123 ]. They also showed elevated expression of PL3 (pectate lyase) during early development (5 h and 16 h), which degrades pectin in the middle lamella of plant cells, supporting adhesion, controlled wall loosening, and early biotrophic establishment without extensive damage [124; Figure 7 and 8 , Supplementary figure 10]. Together, our study reveals that oval and falcate provide their own expression identities despite their shared genome. Furthermore, conidial identity could be maintained even in conidia-derived hyphae after penetration of the host surface, shaping the interaction with the host plant. Such spore type-specific communication in planta has not been described so far for other plant pathogenic fungi and might contribute to the different infection efficacies observed for both conidia types during leaf infection [ 22 ]. Conclusion In conclusion, this study provides a comprehensive understanding of the gene expression profiles of oval and falcate conidia of C. graminicola , the corn anthracnose fungus. Our findings indicate that the distinct infection strategies of both spore types are determined by their unique gene expression profiles, which shape their developmental programs. The identification of spore type-specific expression patterns for genes encoding pathogenicity-related functions, including effectors and carbohydrate-active enzymes (CAZymes), reveals novel insights into the molecular mechanisms underlying the infection process. These results support the notion that oval and falcate conidia-derived hyphae maintain their distinct identities even after germination and induction of pathogenicity processes. Furthermore, our study highlights the importance of considering the physiological and molecular differences between different spore types of a single species when studying plant-pathogen interactions. The results of this study have significant implications for understanding the biology of C. graminicola and its interaction with the plant host, and provide a foundation for the development of new strategies for pest control in the future. Supplementary figures legends Supplementary Figure 1: RNA samples prepared for the RNA-seq analysis and qRT PCR from mycelium, falcate conidia and oval conidia at different development stages of conidia and early maize leaf infection (1 dpi). Supplementary Figure 2: The heatmap displays the expression profiles of the 100 most variable genes in freshly harvested falcate and oval conidia. Red indicates up-regulation and blue indicates down-regulation of gene expression, as determined by iDEP.96 analysis. Supplementary Figure 3: Shown are the 100 most variable genes in falcate and oval conidia following 5h of germination. Expression levels are color-coded, with red representing up-regulated and blue representing down-regulated genes as generated by iDEP.96. Supplementary Figure 4: The heatmap to illustrate the expression patterns of the 100 most variable genes during the germling fusion stage (16h) in falcate and oval conidia. Up-regulated genes are depicted in red and down-regulated in blue, based on iDEP.96 results. Supplementary Figure 5: The heatmap depicts 100 most variable genes in falcate and oval conidia at 1 day post-inoculation (1 dpi) on maize leaves. The heatmap uses red and blue to denote up-regulation and down-regulation, respectively, as calculated with iDEP.96. Supplementary Figure 6: Quantitative real time PCR results for different genes in falcate and oval conidia at different developmental stages and early leaf Infection expressed in terms of log2 fold change values (log2 fold change =1, padj <0.1). Supplementary Figure 7: Transcription factors showing increased expression in oval conidia relative to falcate conidia across different developmental stages. Supplementary Figure 8: Heatmap to illustrate the 100 most variable effector genes that are differentially expressed at different developmental stages in two conidia types. Heatmap was created using the iDEP.96. Red color depicts the up-regulated genes, whereas blue represents the down-regulated genes. Supplementary Figure 9: Volcano plots of top 100 differentially regulated effector genes during early leaf infection by oval or falcate conidia. The volcano plot was generated based on the comparison of gene expression of falcate conidia leaf infection vs freshly harvested falcate conidia (A) and oval conidia leaf infection vs freshly harvested oval conidia (B). VolcaNoseR settings were as followed: fold change threshold: −2 to 2; significant threshold: 2; use thresholds to annotate: changed and significant; criterion for ranking hits: Manhattan distance. Significantly regulated are indicated in red (upregulated) and blue (downregulated). Locus tag numbers of the 10 most relevant hits are indicated. Supplementary Figure 10: Illustration of the detailed expression dynamics (log₂ fold change) of major CAZyme classes, along with their constituent families, across different developmental stages: germination (5 h), germling fusion (16 h), and maize leaf infection (1 dpi), in falcate and oval conidia. Statistical significance denotes differences between conidial typ. The statistical significance indicates the differences between conidial types within each CAZyme class at the respective developmental stage. Supplementary tables Headers Supplementary table 1: Description and mapping rates of RNA-seq samples. Wt in this table refers to CgM2 wild type like strain of Colletotrichum graminicola (M1.001). Supplementary table 2: Pathway enrichment analysis performed for the two spore types under dormant conditions and during maize leaf infection at 1 dpi. Pathway enrichment analysis was performed using the GSEA (preranked) method implemented in iDEP.96, utilizing the fgsea algorithm. Gene sets ranging from 5 to 2000 genes were included. Pathways were considered significant at an FDR cutoff of 0.2. Prior to enrichment analysis, genes with FDR ≥ 1 were excluded. Pathways with green labelled up are up-regulated, whereas pathways with red labelled down are down-regulated in specified samples. Supplementary table 3: Oligonucleotides used in this study. Supplementary files Headers Supplementary file 1: Annotation of protein coding genes of Colletotrichum graminicola M1.001 (CgM2 wildtype strain) with new gene IDs (COGRA5). Supplementary file 2: RNA-seq data of all the Colletotrichum graminicola M1.001 (CgM2 wildtype strain) genes lablled with different locus tags. Data presented was generated using different RNA samples from (mycelium, falcate and oval conidia) at different developmental stages of conidia and maize leaf infection (1 dpi). Supplementary file 3: Expression of Taurine catabolism dioxygenase (TauD) genes in Colletotrichum graminicola M1.001 (CgM2 wildtype strain). Differential expression was determined with a log₂ fold change ≤ –1 or ≥ 1 and an adjusted p-value (padj) < 0.1. Data presented was generated using different RNA samples from (mycelium, falcate and oval conidia) at different developmental stages of conidia and maize leaf infection (1 dpi). Supplementary file 4: RNA-seq data of predicted transcription factors in Colletotrichum graminicola M1.001 (CgM2 wildtype strain), identified using data from 312 predicted transcription factor genes in Neurospora crassa OR74A [ 48 ]. Data presented was generated using different RNA samples from (mycelium, falcate and oval conidia) at different developmental stages of conidia and maize leaf infection (1 dpi). Supplementary file 5: RNA-seq data of predicted effector genes in Colletotrichum graminicola M1.001 (CgM2 wildtype strain). These effector genes were identified using data from [ 17 ]. Data presented was generated using different RNA samples from (mycelium, falcate and oval conidia) at different developmental stages of conidia and maize leaf infection (1 dpi). Supplementary file 6: RNA-seq data of predicted membrane transport proteins in Colletotrichum graminicola M1.001 (CgM2 wildtype strain). They were identified by a multilayered approach using genes that had already been annotated in the genome version v4.0 [ 29 ]. Data presented was generated using different RNA samples from (mycelium, falcate and oval conidia) at different developmental stages of conidia and maize leaf infection (1 dpi). Supplementary file 7: RNA-seq data of predicted CAZymes in Colletotrichum graminicola M1.001 (CgM2 wildtype strain). Data presented was generated using different RNA samples from (mycelium, falcate and oval conidia) at different developmental stages of conidia and maize leaf infection (1 dpi). Supplementary file 8: Expression of LysM candidates (CBM 50) in Colletotrichum graminicola M1.001 (CgM2 wildtype strain). Differential expression was determined with a log₂ fold change ≤ –1 or ≥ 1 and an adjusted p-value (padj) < 0.1. Data presented was generated using different RNA samples from (mycelium, falcate and oval conidia) at different developmental stages of conidia and maize leaf infection (1 dpi). Supplementary file 9: RNA-seq data of predicted developmental genes in Colletotrichum graminicola M1.001 (CgM2 wildtype strain), identified using data based on Sordaria macrospora and Aspergillus flavus NRRL3357 protein sequences [ 84 , 85 ]. Data presented was generated using different RNA samples from (mycelium, falcate and oval conidia) at different developmental stages of conidia and maize leaf infection (1 dpi). Supplementary file 10: RNA-seq data of predicted secondary metabolites biosynthesis clusters in Colletotrichum graminicola M1.001 (CgM2 wildtype strain), which was predicted using antiSMASH version fungiSMASH 7.0.0 [ 51 ]. Data presented was generated using different RNA samples from (mycelium, falcate and oval conidia) at different developmental stages of conidia and maize leaf infection (1 dpi). Acknowledgements We thank Gabriele Beyer and Gertrud Stahlhut for excellent technical assistance and Simone Lewandowski for support with some of the experiments. This work was funded by the Deutsche Forschungsgemeinschaft (Bonn-Bad Godesberg). Grants were provided to D.E.N. (IGRK2172 “PRoTECT” – Projektnummer 273134146), to M.A.G. (Grant No. GU 2252/1-1, Project No. 460261834), and to M.N. (project number NO407/8-1). Additional funding was from a discovery grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) (to J.K.) and by the NSERC-CREATE Program contribution to the PRoTECT program. J.K. is the Power Corporation Fellow of the Canadian Institute for Advanced Research (CIFAR) program on the Fungal Kingdom: Threats & Opportunities. We further thank the Max Planck Institute for Evolutionary Biology for the computing infrastructure. D. R. was supported by the GGNB program PRoTECT. 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